Tag Archives: demography

How many WWII war brides are still living?

Maybe a couple thousand.

European war brides arriving in New York, 1945

European war brides arriving in New York, 1945

Someone should do some new interviews with the World War II “war brides,” because there aren’t very many still living.

I count 1,195 still married and living with their husbands. That means there might be something like 2,000 living if you count widows and those who have remarried. We don’t know exactly how many there were, but various sources put the number at 60,000 or more.

Here’s how I got that current number, using the American Community Survey three-year file, 2010-2012. It’s all the couples who met the following conditions:

  • Married, spouse-present
  • She was born outside the U.S.
  • He was born in the U.S.
  • He is a WWII-era veteran
  • They were married in the years 1941-1945
  • She immigrated in or after the year of their marriage

It’s a pretty simple set of rules.

Some caveats: This doesn’t include any widows or widowers, just those still married (otherwise the ACS doesn’t have any spouse information). I didn’t set a requirement that she be born in a place where American soldiers were during the war (I don’t know all the places they were). I don’t know that all of the WWII-era veterans served outside the U.S. So some of these might not be real war brides, in the sense of women who met and married American military men outside the U.S. during a war.

Still, I think the formula works well. These are the women it turned up:

  • 84% immigrated in 1945 or 1946
  • The age range is 82-94, with a median of 85
  • About two-thirds were under age 20 when they married
  • 61% from the United Kingdom (mostly England)
  • 11% from elsewhere in Western Europe (France, Belgium, Italy)
  • 7% from Eastern Europe (Czechoslovakia, Yugoslavia)
  • The remaining 20% from Canada, Australia/New Zealand, Israel/Palestine, Japan, other)

If you follow my suggestion of finding and interviewing these women or their husbands, here are some other sources you might use:

 

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Different divorce rates

Deadline crush, not getting out the posts I want to. So here instead is one thing I was planning to write about but didn’t really yet.

Photo by Dan Bluestein from Flickr Creative Commons

What’s the rate? Photo by Dan Bluestein from Flickr Creative Commons

I’ve written about divorce quite a bit on here, including on the mess of our official statistics. Now Sheela Kennedy and Steven Ruggles have a (paywalled) paper in the January issue of Demography called, “Breaking Up Is Hard to Count: The Rise of Divorce in the United States, 1980–2010.” Because of the paywall and the obscure academic journal, I thought I had some time to write about it, but it’s been reported on Wonkblog and and other places, so no point in waiting.

The headline is, “divorce is actually on the rise.” It’s risen when they age-standardize the trend, but it’s complicated: “Divorce rates have doubled over the past two decades among persons over age 35. Among the youngest couples, however, divorce rates are stable or declining.” The interpretation is not as simple as, “they have a better measure.”

Meanwhile, I was quoted in a Wall Street Journal story about some TV show, and I let slip my multiple-decrement lifetable version of the current divorce rate. This hasn’t been finished, much less peer-reveiwed, but I’m pretty confident about the basic result. I wrote to the reporter, who asked me for the divorce rate:

As for divorce rates, it’s hard to be definitive because there is no one answer. One answer is: In 2012 there were 19 divorces for every 1000 people who were married (my calculation from the 2012 ACS).

However, what most people want to know is what percentage of people who get married will end up getting divorced. There is no official estimate of this because it involves a guess about the future. We can estimate divorce like we estimate life expectancy — it’s not the actual prediction of how long people will live, it’s how long they would live on average if they lived through the risks of most recent year over and over again their whole lives. (Technically, it’s a projection, not a prediction.) Anyway, using that method, I estimate that about 50% of couples who married in 2012 would eventually divorce (with the rest of the marriages ending with someone’s death).

In her story, of course, that became, simply, “And about half of those who married in 2012 will eventually divorce.”

This method, which I got from this old Sam Preston paper, combines mortality rates and death rates to project how many people are lucky enough to die before divorcing at current rates. (Hence “multiple-decrement,” the demographers’ dry way of saying, “there are only two ways out of this.”) When he applied the method, with much cruder data from 1973, incidentally, he got a 43% divorce rate, which was much higher than the rates floating around at the time, and would have made big news in the blogosphere if there had been one.

More on this eventually.

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What’s in a ratio? Teen birth and marriage edition

Even in our post-apocalypse world, births and marriages are still related, somehow.

Some teenage women get married, and some have babies. Are they the same women? First the relationship between the two across states, then a puzzle.

In the years 2008-2012 combined, 2.5 percent of women ages 15-19 per year had a baby, and 1 percent got married. That is, they were reported in the American Community Survey (IPUMS) to have given birth, or gotten married, in the 12 months before they were surveyed. Here’s the relationship between those two rates across states:

teenbirthmarriage1The teen birth rate  ranges from a low of 1.2 percent in New Hampshire to 4.4 percent in New Mexico. The teen marriage rate ranges from .13 percent in Vermont to 2.3 percent in Idaho.

But how much of these weddings are “shotgun weddings” — those where the marriage takes place after the pregnancy begins? And how many of these births are “gungo-ho marriages” — those where the pregnancy follows immediately after the marriage? (OK, I made that term up.) The ACS, which is wonderful for having these questions, is somewhat maddening in not nailing down the timing more precisely. “In the past 12 months” is all you get.

Here is the relationship between two ratios. The x-axis is percentage of teens who got married who also had a birth (birth/marriage). On the y-axis is the percent of teens who had a birth who also got married (marriage/birth).

teenbirthmarriageIf you can figure out how to interpret these numbers, and the difference between them within states, please post your answer in the comments.

 

 

 

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Especially if they’re Black: A shortage of men for poor women to marry

One thing a lot of liberals and conservatives can agree on: not talking about race.

[If you don't have time for the text, just skip to the figure.]

Liberals are happy when conservatives talk about inequality, which they’re doing a lot more these days. And when they debate marriage as a way to “cure” poverty, neither talks about race. For example, Annie Lowrey writes in the the NYT Magazine:

With Democrats and Republicans pitted against one another in a vicious election-year battle over how to alleviate poverty, marriage is the policy solution du jour.

First, Lowrie makes the now universal mistake in interpreting the famous Chetty et al. result:

In a new study, the economist Raj Chetty and his co-authors found that, in terms of income mobility, nothing matters more for a low-income child than the family structures she sees in her community — not neighborhood segregation, school quality or a host of other factors.

Traditionally in America, when you say “a host of other factors,” that includes race. But the Chetty et al. paper is nearly unique in its avoidance of race, partly because race isn’t specified in tax records. So “nothing matters more” is at best untested, and at worst completely wrong, since race isn’t in the model. (My argument on this is here).

To those of us old enough to remember, or have read stuff from, the 1980s, not including race in this conversation is bizarre. Of course, it is not crazy to talk about poverty as an issue. In that article, Kristi Williams is right when she says:

It isn’t that having a lasting and successful marriage is a cure for living in poverty. Living in poverty is a barrier to having a lasting and successful marriage.

But the article doesn’t address the hard demographic reality that the things that make marriage less available or attractive to poor women — Lowrey lists “globalization, the decline of labor unions, technological change and other tidal economic forces” — have done it much more for Black women, even among the poor. In addition to even worse job prospects, for Black men you need to add incarceration, mortality, and intermarriage rates much higher for men than for women.

Here’s a simple way to see this. Adapting the old formula from William Julius Wilson, I counted up the number of employed, non-married men per non-married woman (employed or not) in the age range 25-34, separately for Blacks and Whites, and by education, for the 50 biggest metropolitan areas (one not shown because of data shortage, one outlier excluded). With intermarriage rates so low for Black women, and the tendency not to marry men without jobs, this is a reasonable approximation of the marriage market for Black women, though it understates the number of men available to White women.

This is the result:

blog-mmpi

Dots in the green areas show relative surpluses of men. Dots under the red line show better markets for White women than for Black women. It takes a minute to figure out. If your jaw dropped, you got it. With or without college degrees Black women face a shortage of “mariageable” men in every single market except five (Portland OR, Minneapolis, Denver, Salt Lake City, and Providence, which was the outlier not shown). For college graduates Black women are under 75 men per 100 women in all but two markets, non-graduates are under 75 in 40 out of 48.

White women’s market is better than Black women’s in all but six (those five plus Sacramento). In most cases White women graduates have a surplus of men from which to choose.

Poverty is one thing. Race is another. They overlap, but on some questions they can’t be combined. Marriage is one of those issues. So, when you talk about the shortage of men to marry, I recommend remembering race.

Note: After I made this graph, Joanna Peppin and I decided to write a paper together on this. That is still in the pipeline, and I was going to save this for when it’s ready. But there will be plenty more.

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State of Utah falsely claims same-sex marriage ban makes married, man-woman parenting more likely

This hasn’t been peer-reviewed, but it’s pretty simple, and I will give the results, data, and code to anyone who wants it. Also, ask me about my low-low expert witness rates ($0 per hour + expenses for federal same-sex marriage cases). If you know the Utah lawyers and they’re looking for this kind of thing, pass it on!

The State of Utah’s “Application to Stay Judgment Pending Appeal,” to stop same-sex marriage from continuing while they appeal their most recent loss, has nothing new to offer, legally. And the social science claims they make are by now a familiar patter of discredited blather, featuring the writing of Regnerus, Wilcox, Blankenhorn, and Allen (follow the links for debunking).

But I either never noticed or never thought about one of their stranger claims, which I felt compelled to debunk. They wrote (excerpting):

A final reason to believe there is a strong likelihood this Court will ultimately invalidate the district court’s injunction is the large and growing body of social science research contradicting the central premise of the district court’s due process and equal protection holdings: i.e., its conclusion (Decision at 2) that there is “no rational reason”—much less any compelling reason—for restricting marriage to opposite-sex couples. That research … confirms … (b) that limiting the definition of marriage to man-woman unions, though it cannot guarantee that outcome, substantially increases the likelihood that children will be raised in such an arrangement. (p. 14)

And then again:

[B]y holding up and encouraging man-woman unions as the preferred arrangement in which to raise children, the State can increase the likelihood that any given child will in fact be raised in such an arrangement. … [T]he district court ignored this fundamental reality. … [p. 18] … By contrast, a State that allows same-gender marriage necessarily loses much of its ability to encourage gender complementarity as the preferred parenting arrangement. And it thereby substantially increases the likelihood that any given child will be raised without the everyday influence of his or her biological mother and father—indeed, without the everyday influence of a father or a mother at all. (p. 17)

Wait a minute. Are they claiming that banning same-sex marriage actually results in more children being raised by married, man-woman couples? Unless you make heterogamous marriage and childbearing compulsory, this doesn’t seem like a sure bet. In fact, now that we have so many people living under the same-sex marriage regime, we can start to investigate this.

Does banning gay marriage work to put kids under heterogamously-married roofs?

Seven states plus the District of Columbia permitted legal same-sex marriage by 2012: Washington, New York, New Hampshire, D.C., Iowa, Vermont, Connecticut, and Massachusetts, which led the way in 2004. And as of very recently we have the 2012 American Community Survey, with ample sample size to assess family structure for every state in every year since 2004.

This analysis is very simple and not a causal analysis of family structure. I am simply testing the assertion by the State of Utah that banning gay marriage “can increase the likelihood that any given child will in fact be raised in such an arrangement.” I do this in a very simple way, and then a pretty simple way.

First, just the raw trends. This shows very simply that children are more likely to live with married parents in states that permit same-sex marriage (red lines) than in states that don’t (blue lines):

ssm-married-kidsI did this both for age 0, to capture marital status at birth, and for all children ages 0-14, to get closer to the concept of “raised.” Here is a table showing the numbers, with the differences calculated, showing exactly how much more likely children are to live with married parents if their states permit same-sex marriage:

ssm-married-kids-table

Whatever the reason, then, children in states that permit same-sex marriage have been 2% – 10% more likely to live with married parents over the last decade. (The same-sex couples themselves do not contribute to this pattern, because the public-use ACS files do not yet count them as married.)

Two potential problems with that as the analysis. First, maybe those states were just more pro-marriage places in the first place (the obvious inference to draw from the fact that they permit same-sex marriage). And second, the declining tendency of children to live with married parents nation-wide might be driving this, as more states join the same-sex marriage pool over time.

To fix these problems, I conducted a simple fixed-effects logistic regression, entering dummy variables for every state and every year into a model predicting whether children live with married parents or not. The only other variable indicates whether the child lives in a state that permits same-sex marriage. By holding constant each state’s average rate, and the national trend over time, the model isolates the statistical association with same-sex marriage legal status. This asks, in essence, whether states that change from not-legal same-sex marriage to legal same-sex marriage have lower or higher odds of their children living with married parents after the change.

Here are the results:

ssm-married-kids-logit

The odds ratios for the same-sex marriage variable are above 1.0, indicating the children in same-sex marriage states are more likely to live with married parents. The effect is not statistically significant from zero at conventional levels for infants, but it is for all children ages 0-14. Again, for whatever reason — it’s not important for this — children are more likely to live with married parents if they live in states where same-sex marriage is legal. All that matters is that the State of Utah’s claim is refuted.

Summarizing all the experience we have data for so far — 34 state-years of data — there is no evidence that allowing same-sex marriage reduces the likelihood that children will be born to or live with married, man-woman parents. If that’s your goal, this policy doesn’t seem to work. (I don’t share that goal, and I especially don’t think it’s relevant to determining legal access to marriage, but they brought it up.)

I’m not the first one to think of this, of course. An earlier analysis in PLoS One found no evidence that same-sex marriage affects the rate of different-sex marriage. That analysis was of marriage, and its most recent data were from 2009. I haven’t seen anyone else do this for children’s living arrangements, and the 2012 only recently became available. If Gary Gates or someone else has done this, please let me know.

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Divorce recession drop rebound, with the 2012 rate

Note: Technical addendum added.

The Census Bureau’s American Community Survey is the best annual national data source for marital events. The 2012 data came out recently, and I don’t believe anyone else has published a divorce rate for 2012. The refined divorce rate – the number of divorces per 1,000 married people – was 19.0 in 2012. Here is the trend since the ACS starting counting divorces:

divrat08-12

What does this mean? It’s a shame the ACS didn’t start counting marital events till 2008, because it means we can’t put that year’s high rate in context. Was it (a) a spike up, suggesting divorce was a leading indicator for the recession; (b) part of a consistent decline, suggesting the the years since have been a pretty substantial increase from the historical trend; or, (c) a data anomaly.*

To put this in the context of the larger trend doesn’t really help answer the question, since we switched from vital records to a national survey, and had a decade with no national statistics in between:

divrate40-12

So, it’s a mystery. My preferred interpretation is still that the recession caused a decline in divorces because disgruntled people were tied up in other crises, couldn’t sell their houses, or couldn’t afford to move out, followed by a rebound of accumulated divorces to our current level.

I published a working paper suggesting this [now forthcoming in Population Research and Policy Review], in which I use 2008 predictors of divorce and estimate that 4% fewer divorces occurred through 2011 compared to what would have been expected had the determinants of divorce not changed in the subsequent years.

My blog series on divorce includes previous reports on rates, and attempts to predict divorce rates using Google searches.

Technical addendum

To replicate my rates for 2012, you start here at the FactFinder, then get the number of married people by sex (ACS Table B12001) and the number of people who got divorced in the 12 months before the survey (ACS Table S1251) — you can enter the table numbers into the search box. There is a slight problem with this, however. Some people who say they got divorced in the past 12 months also say they are currently married (presumably remarried already). Those people are counted twice in the denominator of the FactFinder-based divorce rate — once as divorced people and once as currently married. If you download the public-use file and count those people only once in the denominator, the divorce rate rises by .02 per 1,000 (or 2 people per 100,000) — but this would not change the figures above at the level of precision reported. However, the public-use files produce slightly different estimates than the FactFinder files anyway, because the latter are based on the Census Bureau’s complete file not a subsample, so I use those even though they produce this tiny under-estimate of the divorce rate.

Secondly, what about the difference in divorce rates between men and women? This is a survey, not a vital records count, and there is no way to verify with the now-missing spouses whether they also consider themselves divorced. Maybe they weren’t legally married, or they didn’t really get legally divorced. So there are several possibilities: (a) lots of lesbian divorces, which is unlikely given the small number of lesbian marriages (but note we don’t know the sex of the spouse who is no longer in the household so we can’t distinguish homogamous from heterogamous divorces); (b) women are more likely to describe a breakup as a divorce for reasons unknown; (c) something funky with the survey weights (unweighted divorce rates from the public-use file also show the disparity, but it’s 20% smaller), or; (d) something funky with the sampling.

Who knows! If you are reading this and considering a new career — or a new direction in your existing career — consider becoming a family demographer and helping us figure it out.

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Hell in a handbasket, or the democratization of divorce?

Two ways to look at the results of a new (paywalled) meta-analysis of studies on the educational gradient in divorce rates:

  • Across Europe, most in the liberal welfare states, the privileged access to divorce enjoyed by women with higher education has eroded across the last several decades.
  • Or, led by the welfare state, the liberation of women has progressively destroyed families further and further down the economic food chain.

The study combined many analyses of divorce rates and analyzed them together. The results for the Nordic countries were most pronounced. Here is the relationship between education and divorce in studies over the last 20 years (going down the chart). Dots to the right of the solid line indicate studies in which more educated women had higher odds of divorce, moving to the left means divorce is spreading to women with less education:

divorcemeta

Studies now show a negative gradient, that is, women with less education have higher odds of divorce. The trend was in the same direction in most of Europe, but not as advanced. (In the U.S., incidentally, which was not included in the analysis, we have a curvilinear pattern, with the highest rates among the some-college population, and the lowest among those with advanced degrees. I have a preliminary paper here, and a subsequent version under review.)

Raising the question, how much divorce is the right amount? Some people treat divorce like child abuse — any amount is bad. But can’t we agree there was not enough divorce 50 years ago — meaning people who were in miserable marriages couldn’t get out of them? And, given it was concentrated among more privileged families, wasn’t that evidence of social class privilege? So, what’s the right balance? You might think no education effect is the best, with marriages equally likely to end in divorce regardless of social class. But what if the marriages of poor people have more problems, and they need or want divorce more?

The analysis further showed that the shift in the education gradient was correlated with the overall divorce rate (as divorce increased, it democratized) and with the labor force participation rate for women (the more employed women, the more divorce spread to the lower classes). Divorce laws had no effect.

We shouldn’t assume any increase in divorce is bad. Maybe it’s like living alone: the people who do it are often not happy with their situation, and it often means something has gone wrong for them, but having the option is better than not.

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Fewer children, more employed women: International edition

In the discussion on this post about interpreting historical trends, several people pointed out that the relationship between fertility rates and women’s employment rates is not simple, and has changed, at least in the rich countries. I made some charts using international data about that, which I will show below.

But first a figure from this paper by Rense Nieuwenhuis and colleagues, which he linked from the comments. In that 2012 paper they show that the negative association between motherhood and employment weakened in OECD countries from 1975 to 1999. Still, at the individual level, in almost every country and every year, the odds of being employed are lower for mothers, as this figure shows (dots lower in each box indicate a bigger employment gap between mothers and non-mothers; click to enlarge):

oecd

It’s a very interesting paper I should have recommended earlier.

The fact that mothers are less likely to be employed than women without children doesn’t mean that countries — or time periods — with lower fertility rates necessarily have higher women’s employment rates (see Nieuwenhuis’s comment for a few other papers on this). So it’s good to look at individual as well as macro-level patterns.

Anyway, those are all rich countries. What about poorer countries? Because of the unbelievably good archive of census data (freely available, thank gov) at IPUMS International (74 countries, 238 censuses, 544 million records, and counting), it’s possible to ask questions like this.

Looking for censuses that recorded the number of children ever born to women, as well as their employment status, I sampled 10,000 households each from 89 censuses in 29 countries in Latin America or the Caribbean, Asia, and Africa, ranging in time period from 1960 to 2010. I limited the samples to women ages 25-44, and counted their children up to 7. The countries were:

  • Latin America / Caribbean: Argentina, Bolivia, Brazil, Cambodia, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Haiti, Jamaica, Mexico, Nicaragua, Panama, Peru, Uruguay
  • Africa: Burkina Faso, Ghana, Guinea, Kenya, Malawi, Morocco, Rwanda, Senegal, Sierra Leone, South Africa
  • Asia: China, Indonesia, Vietnam

Here’s what I found. Overall there is not a strong correlation at the country level between mean number of children born per women and employment rates (correlation = -.09):

wlfp1

Closer inspection reveals a pretty strong relationship in the Latin America / Caribbean samples, as well as the three Asian countries, but not the African samples. But this scatter doesn’t show the time trends. If I limit it to the 9 countries that have at least 4 censuses (8 from Latina America, plus Indonesia), they almost all show the pattern I started with: falling fertility and rising women’s employment rates. The arrows track each country’s censuses in chronological order, so moving up and to the left fits the historical pattern:

wlfp2The country-level association is not the same as an individual-level association, because it can’t confirm that women with more children themselves are the ones who aren’t employed. To gauge that I estimate a linear regression within each census, measuring the association between number of children ever born and employment, controlling only for age. These are the results from those 89 regressions. The x-axis is still the mean number of children in each sample, but now the y-axis is the statistical effect of each additional child on the probability of being employed: below 0 indicates that having had more children reduces the probability of employment.

wlfp3In 15 of the 89 samples, each additional child is associated with a greater chance the woman is employed, but in 74 samples the effect is negative*. Furthermore, it appears that countries with lower fertility rates have a stronger negative association between children and employment — each kid reduces the odds of employment more. Consider, though, that a reduction of .11 in the probability of employment for each kid has a lower total effect in a country with two children per mother than a reduction of .05 in a country where people have three kids each**.

If we go back to the 9 countries with at least 4 censuses each, we can compare the trends in fertility to the child effect on employment:

wlfp4Most of these countries (Chile, Colombia, Indonesia, Panama, and Mexico) show the pattern in which the child effect strengthened while the fertility rate fell. Uruguay and Argentina show falling child effects and little fertility change.

Two possible conclusions:

  1. Although it may seem prosaic, this reminds me that the long-run, modern movement of women into the paid labor force is closely associated with the decline in fertility (as well as, incidentally, the decline in marriage). I think of that as indicating that women’s labor is increasingly diffused outward from their own children through market (or otherwise socialized) mechanisms. As the prototype, think of a woman with 2 children teaching 30 children in school (while her own kids are in another classroom) instead of spending the day caring for 6 children at home (while growing food, etc.).
  2. The trend toward a smaller employment gap between mothers and non-mothers is a recent, selective, rich-country phenomenon associated with very low fertility rates and (as the Nieuwenhuis et al. paper nicely shows) state policies designed to encourage mothers’ labor force participation (and, they hope, increase fertility).

Footnotes:

* I didn’t bother with significance tests because these were arbitrarily small subsamples from each census; we could always go test them with the full samples.

** I could test a total motherhood effect, like Nieuwenhuis et al. did, but in almost all of these are samples 80% or 90% of women have children, so the kid/no-kid comparison is not as salient.

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Suzanne Bianchi

suzannepaa

Suzanne Bianchi died on November 4th. She was diagnosed with pancreatic cancer in July. Beyond her family and friends, Suzanne’s death is a tremendous loss for family demography and sociology, to which she contributed so much, and to the network of collaborators, students, and former students that she nurtured during her too-short career.

After completing her PhD at the University of Michigan in 1978, she spent 16 years working at the U.S. Census Bureau before joining the faculty at the University of Maryland in 1994. In 2009 she moved to UCLA. In 2000 she was president of the Population Association of America. (Google Scholar profile, UCLA profile, UMD profile.)

I met her at Maryland in 1995, where I took her seminar, Demography of the Labor Force, and she served on my dissertation committee in 1999. We wrote an article together in 1999, and I contributed to another one in 2004. But those bio details don’t tell the story of her impact on my life and career, or those of so many other students.

From my own first job at Census to my move back to Maryland, I haven’t made a major career decision in the last 20 years without consulting her, and for good reason — for a smart, selfless, well-centered interpretation of what was going on, no one was better. Hers was the rare ability to do great social science and great personal interaction, and she cared deeply about both.

suzanne-group

Back: Ching-Yi Shieh, Rose Kreider, Aparna Sundaram, Suzanne Bianchi, Liana Sayer, Philip Cohen. Front: Soumya Alva, Chunnong Saeger, Lekha Subaiya, Jane Lawler Dye, Marybeth Mattingly

This picture, probably from the 2000 Population Association conference, hints at her influence on the students with whom she worked (this CV lists her students through 2011). In academia, policy and demographic practice, the field is littered with people who learned from her and worked with her, directly or indirectly. These are 57 of her co-authors:

Katharine Abraham
Mary Allard
Christine Bachrach
Michael Bittman
Caroline Bledsoe
Lynne Casper
Lindsay Chase-Lansdale
Philip Cohen
Diana Colasanto
Thomas DiPrete
Jane Dye
Paula England
Reynolds Farley
Javier Garcia-Manglano
Shirley Hatchett
Howard Hayghe
Sandra Hofferth
Joseph Hotz
Kristin Hunt
Joan Kahn
Sarah Kendig
Laurent Lesnard
Judith Lichtenberg
Aaron Maitland
Marybeth Mattingly
Kathleen McGarry
Brittany McGill
Melissa Milkie
Kristin Moore
Philip Morgan
Tiziana Nazio
Kei Nomaguchi
Pia Peltola
Joseph Pleck
Joe Price
Tetyana Pudrovska
Yeu Qiu
Sara Raley
John Robinson
Carolyn Rogers
Nancy Rytina
Seth Sanders
Liana Sayer
Howard Schuman
Judith Seltzer
Daphne Spain
Jay Stewart
Charles Strohm
Jeffrey Stueve
Lekha Subaiya
Duncan Thomas
Betsy Thorn
Robert Wachbroit
Wendy Wang
David Wasserman
Vanessa Wight
Jenjira Yahirun

Suzanne’s presidential address was titled, “Maternal Employment and Time with Children: Dramatic Change or Surprising Continuity?” If you’re reading this you are probably familiar with it. She reported that, despite dire warnings of imminent harm to children — and countless empirical searches for that harm — the evidence was that women’s employment did not harm their children, perhaps because it wasn’t leading to parents spending less time with them. Instead, lower fertility, changing definitions of ideal childhood, time juggling by parents, and increasing father time had kept parental time with children roughly constant. Plus, parents didn’t spend as much time with their kids in the old days as researchers generally assumed anyway. Her address changed the field, and helped open up research into the dynamics of family time use, which had often been black-boxed as simply non-employed time.

As I thought about her own time cut suddenly short and reread that article, I caught on the last paragraph. With her typical balance of clear-eyed yet completely compassionate, she concluded:

My one concern is that I have given the impression that women have found it quite easy to balance increased labor force participation with child rearing, to reduce hours of employment so as to juggle childcare, and to get their husbands more involved in child rearing; and that fathers have found it easy to add more hours with children to those they already commit to supporting children financially. I do not think these changes have been easy for American families, particularly for American women. Why have women so increased their hours of paid employment? Many observers would emphasize constraints — men’s poor labor force prospects — and this is probably part of the story. But this explanation is not sufficient, for it gives too little attention to the dramatic change in opportunities for women and in women’s own conceptions of what a successful, normal adulthood should entail. Yet I suspect that every mother has felt self-doubt about the path taken, and has been concerned about whether she has done the best thing for herself and/or her children, and that these feelings continue to give women pause and to slow change both in the marketplace and at home.

I’m sure she was reflecting in part on her own life and career as she delivered that speech — at the pinnacle of her career, with her family in attendance. Her life embodied that transformation — those opportunities, and that self-reflection — and in her career she made an indelible contribution both to our understanding of this newer world, and to the lives of many people making their way within it.

Update: Since I wrote this, other obituaries and tributes have appeared. Here are a few:

 

 

 

 

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What’s with the historical trend juxtapositions?

When is it OK to juxtapose historical trends?

You have to watch out for this (via Boing Boing):

There is no reason to suspect that the rise in autism is linked to the rise in organic food sales.

But other times it seems reasonable to me, like this:

fert-wlfp-trend

There are lots of reasons the long-run decline in fertility is related to the rise in women’s employment rates. We know from lots of research that women with more children are less likely to have jobs; women with jobs are less likely to have children; and over time the proportion of women in the second group has grown relative to the first.

So it’s OK to use eyeballed historical trends when you have good research backing up the association. Your conclusions, then, don’t rely on the simple trend comparison. The trends are an illustration.

But trends need not have have a simple cause-and-effect relationship — or a unidirectional relationship — for it to be important to compare them. Sometimes the relationship is just descriptively important. So, looking at the graph above, it would be reasonable to say, “Women’s lives sure have changed. They have fewer children and more jobs, on average, than they used to.”

And then there is the negative case. The other day I complained when Kay Hymowitz implied that the rise in father-absent families caused an increase in crime among boys. And I offered this simple trend comparison to undermine that story:

It was not my intention to say there is no connection between father absence and boys’ criminal behavior. (I’ve sketched out some possible links in this old post; and made essentially the same comparison about single mothers and crime before.) But the lack of a strong correlation in the trends over time is a challenge. That’s what I’ve been arguing about cell phones and traffic accidents:

Of course driving and texting is dangerous. And of course single parents have a harder time (on average) supervising and disciplining their children than married parents. But if there is a big discordance between the trends — texting and driving, single parents and crime — then that’s a problem for the story that one trend is driven by the other. Causal relationships may be apparent in a lab, or at the margins, but to explain large-scale social change is more difficult — and that’s often what we’re trying to do when we draw from specific research to make political, policy, or theoretical arguments.

So, it’s OK to use discordant trends to take potshots at a proposed causal story, to express skepticism. The discordant trends are a hurdle for the theory to overcome.

If you have good research showing that single parenthood, and especially father absence, is harming boys more than girls, then it would be to OK to use trends as an illustration. It just can’t be your main evidence. So Kay Hymowitz could reasonably include a graph like this to accompany her extensive review of the research on family structure and trouble for boys:

hymowitz-response2

Yes, women’s advantage in high school and college completion has accompanied the trend toward father-absent living arrangements for young boys. That doesn’t fulfill her need to present more direct evidence, but it’s a piece of supporting evidence.

Conclusion: Juxtaposing historical trends is not how you prove a theory. It is a great tool for illustrating known associations, for describing social change, and for challenging theories or narratives.

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